Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

This paper investigates the problem of partitioning a shared cache between multiple concurrently executing applications. The commonly used LRU policy implicitly partitions a shared cache on a demand basis, giving more cache resources to the application that has a high demand and fewer cache resources to the application that has a low demand. However, a higher demand for cache resources does not always correlate with a higher performance from additional cache resources. It is beneficial for performance to invest cache resources in the application that benefits more from the cache resources rather than in the application that has more demand for the cache resources. This paper proposes utility-based cache partitioning (UCP), a low-overhead, runtime mechanism that partitions a shared cache between multiple applications depending on the reduction in cache misses that each application is likely to obtain for a given amount of cache resources. The proposed mechanism monitors each application at runtime using a novel, cost-effective, hardware circuit that requires less than 2kB of storage. The information collected by the monitoring circuits is used by a partitioning algorithm to decide the amount of cache resources allocated to each application. Our evaluation, with 20 multiprogrammed workloads, shows that UCP improves performance of a dual-core system by up to 23% and on average 11% over LRU-based cache partitioning.

/pdf/utility-based-cache-partitioning-a-low-overhead-high-4t50s5xud2.pdf

Utility-Based Cache Partitioning: A Low-Overhead, High-Performance, Runtime Mechanism to Partition Shared Caches

A significant part of future microprocessor real estate will be dedicated to L2 or L3 caches. These on-chip caches will heavily impact processor perfor- mance, power dissipation, and thermal management strategies. There are a number of interconnect design considerations that influence power/performance/area characteristics of large caches, such as wire mod- els (width/spacing/repeaters), signaling strategy (RC/differential/transmission), router design, etc. Yet, to date, there exists no analytical tool that takes all of these parameters into account to carry out a design space exploration for large caches and estimate an optimal organization. In this work, we implement two major extensions to the CACTI cache modeling tool that focus on interconnect design for a large cache. First, we add the ability to model different types of wires, such as RC-based wires with different power/delay characteristics and differential low-swing buses. Second, we add the ability to model Non-uniform Cache Access (NUCA). We not only adopt state-of-the-art design space exploration strategies for NUCA, we also enhance this exploration by considering on-chip network contention and a wider spectrum of wiring and routing choices. We present a validation analysis of the new tool (to be released as CACTI 6.0) and present a case study to showcase how the tool can improve architecture research methodologies. Keywords: cache models, non-uniform cache archi- tectures (NUCA), memory hierarchies, on-chip intercon- nects.

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Optimizing NUCA Organizations and Wiring Alternatives for Large Caches with CACTI 6.0

Is your memory hierarchy stopping your microprocessor from performing at the high level it should be? Memory Systems: Cache, DRAM, Disk shows you how to resolve this problem. The book tells you everything you need to know about the logical design and operation, physical design and operation, performance characteristics and resulting design trade-offs, and the energy consumption of modern memory hierarchies. You learn how to to tackle the challenging optimization problems that result from the side-effects that can appear at any point in the entire hierarchy.As a result you will be able to design and emulate the entire memory hierarchy. . Understand all levels of the system hierarchy -Xcache, DRAM, and disk. . Evaluate the system-level effects of all design choices. . Model performance and energy consumption for each component in the memory hierarchy.

Memory Systems: Cache, DRAM, Disk

One of the most effective techniques to reduce a processor’s power consumption is to reduce supply voltage. However, reducing voltage in the context of manufacturing-induced parameter variations cancause many types of memory circuits to fail. As a result, voltage scaling is limited by a minimum voltage, often called Vccmin, beyond which circuits may not operate reliably. Large memory structures (e.g., caches) typically set Vccmin for the whole processor. In this paper, we propose two architectural techniques that enable microprocessor caches (L1and L2), to operate at low voltages despite very high memory cell failure rates. The Word-disable scheme combines two consecutive cache lines, to form a single cache line where only non-failing words are used. The Bit-fix scheme uses a quarter of the ways in a cache set to store positions and fix bits for failing bits in other ways of the set. During high voltage operation, both schemes allow use of the entire cache. During low voltage operation, they sacrifice cache capacity by 50% and 25%, respectively, to reduce Vccmin below 500mV. Compared to current designs with a Vccmin of 825mV, our schemes enable a 40% voltage reduction, which reduces power by 85% and energy per instruction (EPI) by 53%

/pdf/trading-off-cache-capacity-for-reliability-to-enable-low-2ktu5ocrkn.pdf

Trading off Cache Capacity for Reliability to Enable Low Voltage Operation

Chip multiprocessors (CMPs) substantially increase capacity pressure on the on-chip memory hierarchy while requiring fast access. Neither private nor shared caches can provide both large capacity and fast access in CMPs. We observe that compared to symmetric multiprocessors (SMPs), CMPs change the latency-capacity tradeoff in two significant ways. We propose three novel ideas to exploit the changes: (1) Though placing copies close to requestors allows fast access for read-only sharing, the copies also reduce the already-limited on-chip capacity in CMPs. We propose controlled replication to reduce capacity pressure by not making extra copies in some cases, and obtaining the data from an existing on-chip copy. This option is not suitable for SMPs because obtaining data from another processor is expensive and capacity is not limited to on-chip storage. (2) Unlike SMPs, CMPs allow fast on-chip communication between processors for read-write sharing. Instead of incurring slow access to read-write shared data through coherence misses as do SMPs, we propose in-situ communication to provide fast access without making copies or incurring coherence misses. (3) Accessing neighborsý caches is not as expensive in CMPs as it is in SMPs. We propose capacity stealing in which private data that exceeds a coreýs capacity is placed in a neighboring cache with less capacity demand. To incorporate our ideas, we use a hybrid of private, per-processor tag arrays and a shared data array. Because the shared data array is slow, we employ non-uniform access and distance associativity from previous proposals to hold frequently-accessed data in regions close to the requestor. We extend the previously-proposed Non-uniform access with Replacement And Placement usIng Distance associativity (NuRAPID) to CMPs, and call our cache CMP-NuRAPID. Our results show that for a 4-core CMP with 8 MB cache, CMP-NuRAPID improves performance by 13% over a shared cache and 8% over private caches for three commercial multithreaded workloads.

/pdf/optimizing-replication-communication-and-capacity-allocation-4zso50j1uf.pdf

Optimizing Replication, Communication, and Capacity Allocation in CMPs

Technology advancements have enabled the integration of large on-die embedded DRAM (eDRAM) caches. eDRAM is significantly denser than traditional SRAMs, but must be periodically refreshed to retain data. Like SRAM, eDRAM is susceptible to device variations, which play a role in determining refresh time for eDRAM cells. Refresh power potentially represents a large fraction of overall system power, particularly during low-power states when the CPU is idle. Future designs need to reduce cache power without incurring the high cost of flushing cache data when entering low-power states. In this paper, we show the significant impact of variations on refresh time and cache power consumption for large eDRAM caches. We propose Hi-ECC, a technique that incorporates multi-bit error-correcting codes to significantly reduce refresh rate. Multi-bit error-correcting codes usually have a complex decoder design and high storage cost. Hi-ECC avoids the decoder complexity by using strong ECC codes to identify and disable sections of the cache with multi-bit failures, while providing efficient single-bit error correction for the common case. Hi-ECC includes additional optimizations that allow us to amortize the storage cost of the code over large data words, providing the benefit of multi-bit correction at same storage cost as a single-bit error-correcting (SECDED) code (2% overhead). Our proposal achieves a 93% reduction in refresh power vs. a baseline eDRAM cache without error correcting capability, and a 66% reduction in refresh power vs. a system using SECDED codes.

/pdf/reducing-cache-power-with-low-cost-multi-bit-error-3b7cc0vfu7.pdf

Reducing cache power with low-cost, multi-bit error-correcting codes

Wire delays continue to grow as the dominant component oflatency for large caches.A recent work proposed an adaptive,non-uniform cache architecture (NUCA) to manage large, on-chipcaches.By exploiting the variation in access time acrosswidely-spaced subarrays, NUCA allows fast access to closesubarrays while retaining slow access to far subarrays.Whilethe idea of NUCA is attractive, NUCA does not employ designchoices commonly used in large caches, such as sequential tag-dataaccess for low power.Moreover, NUCA couples dataplacement with tag placement foregoing the flexibility of dataplacement and replacement that is possible in a non-uniformaccess cache.Consequently, NUCA can place only a few blockswithin a given cache set in the fastest subarrays, and mustemploy a high-bandwidth switched network to swap blockswithin the cache for high performance.In this paper, we proposethe Non-uniform access with Replacement And PlacementusIng Distance associativity" cache, or NuRAPID, whichleverages sequential tag-data access to decouple data placementfrom tag placement.Distance associativity, the placementof data at a certain distance (and latency), is separated from setassociativity, the placement of tags within a set.This decouplingenables NuRAPID to place flexibly the vast majority offrequently-accessed data in the fastest subarrays, with fewerswaps than NUCA.Distance associativity fundamentallychanges the trade-offs made by NUCA's best-performingdesign, resulting in higher performance and substantiallylower cache energy.A one-ported, non-banked NuRAPIDcache improves performance by 3% on average and up to 15%compared to a multi-banked NUCA with an infinite-bandwidthswitched network, while reducing L2 cache energy by 77%.

/pdf/distance-associativity-for-high-performance-energy-efficient-1wylnic590.pdf

Distance associativity for high-performance energy-efficient non-uniform cache architectures

Modern DRAM cells are periodically refreshed to prevent data loss due to leakage. Commodity DDR (double data rate) DRAM refreshes cells at the rank level. This degrades performance significantly because it prevents an entire DRAM rank from serving memory requests while being refreshed. DRAM designed for mobile platforms, LPDDR (low power DDR) DRAM, supports an enhanced mode, called per-bank refresh, that refreshes cells at the bank level. This enables a bank to be accessed while another in the same rank is being refreshed, alleviating part of the negative performance impact of refreshes. Unfortunately, there are two shortcomings of per-bank refresh employed in today's systems. First, we observe that the perbank refresh scheduling scheme does not exploit the full potential of overlapping refreshes with accesses across banks because it restricts the banks to be refreshed in a sequential round-robin order. Second, accesses to a bank that is being refreshed have to wait. To mitigate the negative performance impact of DRAM refresh, we propose two complementary mechanisms, DARP (Dynamic Access Refresh Parallelization) and SARP (Subarray Access Refresh Parallelization). The goal is to address the drawbacks of per-bank refresh by building more efficient techniques to parallelize refreshes and accesses within DRAM. First, instead of issuing per-bank refreshes in a round-robin order, as it is done today, DARP issues per-bank refreshes to idle banks in an out-of-order manner. Furthermore, DARP proactively schedules refreshes during intervals when a batch of writes are draining to DRAM. Second, SARP exploits the existence of mostly-independent subarrays within a bank. With minor modifications to DRAM organization, it allows a bank to serve memory accesses to an idle subarray while another subarray is being refreshed. Extensive evaluations on a wide variety of workloads and systems show that our mechanisms improve system performance (and energy efficiency) compared to three state-of-the-art refresh policies and the performance benefit increases as DRAM density increases.

/pdf/improving-dram-performance-by-parallelizing-refreshes-with-56std5crsf.pdf

Zeshan A. Chishti

Papers

Trading off Cache Capacity for Reliability to Enable Low Voltage Operation

Optimizing Replication, Communication, and Capacity Allocation in CMPs

Reducing cache power with low-cost, multi-bit error-correcting codes

Distance associativity for high-performance energy-efficient non-uniform cache architectures

Improving DRAM performance by parallelizing refreshes with accesses